Prediction of non-methane hydrocarbons in Kuwait using regression and Bayesian kriged Kalman model

This article describes the hierarchical Bayesian approach for predicting average hourly concentrations of ambient non-methane hydrocarbons (NMHC) in Kuwait where records of six monitor stations located in different sites are observed at successive time points. Our objective is to predict the concentration level of NMHC in unmonitored areas. Here an attempt is made for the prediction of unmeasured concentration of NMHC at two additional locations in Kuwait. We will implement a kriged Kalman filter (KKF) hierarchical Bayesian approach assuming a Gaussian random field, a technique that allows the pooling of data from different sites in order to predict the exposure of the NMHC in different regions of Kuwait. In order to increase the accuracy of the KKF we will use other statistical models such as imputation, regression, principal components, and time series analysis in our approach. We considered four different types of imputation techniques to address the missing data. At the primary level, the logarithmic field is modeled as a trend plus Gaussian stochastic residual model. The trend model depends on hourly meteorological predictors which are common to all sites. The residuals are then modeled using KKF, and the prediction equation is derived conditioned on adjoining hours. On this basis we developed a spatial predictive distribution for these residuals at unmonitored sites. By transforming the predicted residuals back to the original data scales, we can impute Kuwait’s hourly non-methane hydrocarbons field.     

燕山石化地区NMHC的特征研究

本文采用气相色谱－质谱联用方法，对燕山石化地区不同生产区域的大气样品，定量地进行非甲烷碳氢化合物（ＮＭＨＣ）的分类测定。本方法可以鉴定Ｃ２－Ｃ１１共六十多个色谱峰。实验表明，燕山石化地区烷烃、烯烃和芳香烃等各类碳氢化合物均具有较高浓度，是对ＮＭＨＣ有重要贡献的一个工业源。本文还参照各大气样品的不同来源，分析了ＮＭＨＣ的排放特征，从而确定了燕山石化区ＮＭＨＣ排放的典型物种。     

Analysis of air pollution at Shuaiba industrial area in Kuwait

A study has been conducted over a period of one year on measurements of air pollution in the Shuaiba Industrial Area (SIA) of Kuwait. The study included analysis of pollutant behaviour relative to the wind speed and direction. SIA comprises several large scale industries including three petroleum refineries, two power plants, two fertilizer plants, a cement plant, a chlorine and soda plant, a commercial harbour and two large oil loading terminals. Measurements of 15 parameters have been carried out every 5 minutes using a mobile laboratory fitted with an automatic calibrator and a data storage system. The pollutants studied include methane, non‐methane hydrocarbons (NMHC), carbon monoxide, carbon dioxide, nitrogen oxides (NO, NO₂, and NO _x ), sulphur dioxide, ozone and suspended dust. Meteorological parameters monitored simultaneously include wind speed and direction, air temperature, relative humidity, solar radiation, and barometric pressure. The air quality data collected using the mobile laboratory have been used to calculate the diurnal and monthly variations in the major primary and secondary pollutants. Distribution levels of these pollutants relative to wind direction and speed have also been used in the analysis. The results show large diurnal variations in some pollutant concentrations. Generally, two types of concentration variations have been found, depending on whether the species is a primary or a secondary pollutant. Diurnal variations with two maxima were observed in the concentrations of primary pollutants including NO, SO₂, NMHC, CO and suspended dust, whereas a single maximum was observed for secondary pollutants such as O₃and NO₂. The monthly variations of SO₂and NO _x showed maximum values during the warm months. However, ozone showed a quite marked seasonal variation with maxima during spring and late summer and a minimum during the early summer. The results also indicated a common source for NO _x , SO₂, NMHC, CO and suspended dust to the North‐West (NW) of the monitoring station. Moreover for NO _x and SO₂, another less significant source is to the South‐South‐West (SSW) and South‐West (SW) of the monitoring station.     

An assessment of the air pollution data from two monitoring stations in Kuwait

This article assesses the air pollution data from two monitoring stations in Kuwait. The measurements cover major pollutants, i.e., CO, CO₂, methanated and non-methanated hydrocarbons, NO _x , SO₂, O₃, and particulate matter (PM10). The data also includes meteorological parameters, i.e., solar intensity, temperature, wind speed, and wind direction, and has been collected over a period 4 years, from 2001 to 2004. Data analysis includes the assessment of annual hourly averages and 1-h maxima. Typical pollutant concentration trends, similar to those previously reported for Kuwait and for other locations around the world, are observed except for particulate matter measurements, which have higher values because of proximity to the desert. Emissions of nitrogen oxides show a consistent increase over the years. This is caused by the increase in the number of motor vehicles and the expansion in power generation and industrial activities. The data collected is a subset of the air quality criteria, as defined by the US EPA (United States Environmental Protection Agency).     

Lifting a veil on diversity: a Bayesian approach to fitting relative-abundance models.

Bayesian methods incorporate prior knowledge into a statistical analysis. This prior knowledge is usually restricted to assumptions regarding the form of probability distributions of the parameters of interest, leaving their values to be determined mainly through the data. Here we show how a Bayesian approach can be applied to the problem of drawing inference regarding species abundance distributions and comparing diversity indices between sites. The classic log series and the lognormal models of relative- abundance distribution are apparently quite different in form. The first is a sampling distribution while the other is a model of abundance of the underlying population. Bayesian methods help unite these two models in a common framework. Markov chain Monte Carlo simulation can be used to fit both distributions as small hierarchical models with shared common assumptions. Sampling error can be assumed to follow a Poisson distribution. Species not found in a sample, but suspected to be present in the region or community of interest, can be given zero abundance. This not only simplifies the process of model fitting, but also provides a convenient way of calculating confidence intervals for diversity indices. The method is especially useful when a comparison of species diversity between sites with different sample sizes is the key motivation behind the research. We illustrate the potential of the approach using data on fruit-feeding butterflies in southern Mexico. We conclude that, once all assumptions have been made transparent, a single data set may provide support for the belief that diversity is negatively affected by anthropogenic forest disturbance. Bayesian methods help to apply theory regarding the distribution of abundance in ecological communities to applied conservation.     

Improving multi-site benefit functions via Bayesian model averaging: A new approach to benefit transfer

A benefit function transfer obtains estimates of willingness-to-pay (WTP) for the evaluation of a given policy at a site by combining existing information from different study sites. This has the advantage that more efficient estimates are obtained, but it relies on the assumption that the heterogeneity between sites is appropriately captured in the benefit transfer model. A more expensive alternative to estimate WTP is to analyze only data from the policy site in question while ignoring information from other sites. We make use of the fact that these two choices can be viewed as a model selection problem and extend the set of models to allow for the hypothesis that the benefit function is only applicable to a subset of sites. We show how Bayesian model averaging (BMA) techniques can be used to optimally combine information from all models.The Bayesian algorithm searches for the set of sites that can form the basis for estimating a benefit function and reveals whether such information can be transferred to new sites for which only a small data set is available. We illustrate the method with a sample of 42 forests from U.K. and Ireland. We find that BMA benefit function transfer produces reliable estimates and can increase about 8 times the information content of a small sample when the forest is ‘poolable’.     

Extreme value analysis of Munich air pollution data

We present three different approaches to modelling extreme values of daily air pollution data. We fitted a generalized extreme value distribution to the monthly maxima of daily concentration measures. For the exceedances of a high threshold depending on the data, the parameters of the generalized Pareto distribution were estimated. Accounting for autocorrelation, clusters of exceedances were used. To obtain information about the relationship of the exceedance of the air quality standard and possible predictors we applied logistic regression. Results and their interpretation are given for daily average concentrations of ozone and nitrogen dioxide at two monitoring sites within the city of Munich.     

Korrelation meteorologischer und pflanzenphänologischer Indikatoren des Klimawandels

Goal and Scope

While the rise of the air temperature as part of the global climatic change seems to be widely assured, questions about the effects of this phenomenon in ecological systems get more and more interesting. In this sense this article shows by the example of monitoring sites in Baden-Württemberg the correlation of air temperature measurements with data on the phenology of selected plants.

Methods

To this end the data on air remperature and plant phenology which are gathered from spatial dislocated measurement sites were correlated by prior application of Kriging interpolation. In addition, geostatistics are ssed to analyze and cartographically depict the spatial structure of the phenology in spring and in summer.

Results and Conclusion

The statistical analysis reveals a significant relationship between the rising air temperature and the early beginning phenological phases like blooming or fruit maturation. From 1991 to 1999 spring time as indicated by phenological phases begins up to 14 days earlier than from 1961 to 1990. As proved by geostatistics, this holds true for the whole territory of Baden-Württemberg.

Recommendation and Perspective

The effects of the rise of air temperature should be examined not only by monitoring of biological individuals as for example plants, but on ecosystem level, too. The German ecosystem research and the environmental monitoring should be supplemented by the study of the effects of the climatic change in ecosystems. Because air temperature and humidity have a great influence on the temporal and spatial distribution of pathogen carriers (vectors) and pathogens mapping of the determinants of vector and pathogen distribution in space and time should be done in order to identify hot spots for risk assessment and further detailed epidemiological studies.     

Planning for the monitoring of pollutants from non‐stationary combustion sources†

The most important constituents of traffic exhaust are carbon monoxide, nitrogen oxides, volatile organic compounds, polycyclic hydrocarbons and lead. Satisfactory analytical methods are available for measuring these compounds, but the required expenditure in their application to air pollution studies is quite different. Due to correlations between the concentrations of several exhaust components in road air conclusions regarding the expected level of some substances can be drawn by measurement of another emitted compound. But the selection of indicator compounds must be made under the aspect of the source‐specific relationship of emitted substances. The temporal and spatial distribution of air pollutants in street air is affected by traffic emissions more than by meteorological conditions. “Fingerprints”; of typical organic gaseous components of traffic exhaust also were found in residential areas.     

Bayesian estimation of age-specific bird nest survival rates with categorical covariates

The populations of many North American landbirds are showing signs of declining. Gathering information on breeding productivity allows critical assessment of population performance and helps identify good habitat-management practices. He (Biometrics (2003) 59 962–973) proposed a Bayesian model to estimate the age-specific nest survival rates. The model allows irregular visiting schedule under the assumption that the observed nests have homogeneous nest survival. Because nest survival studies are often conducted in different sites and time periods, it is not realistic to assume homogeneous nest survival. In this paper, we extend He’s model by incorporating these factors as categorical covariates. The simulation results show that the Bayesian hierarchical model can produce satisfactory estimates on nest survival and capture different factor effects. Finally the model is applied to a Missouri red-winged blackbird data set.     

Characterization and source apportionment of polycyclic aromatic hydrocarbons in the ambient air (Tehran,Iran)

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in the urban atmosphere and the atmospheric pollutants to be identified as the most suspected carcinogens. In early measurements of urban PAH concentrations in Tehran area, the correlation between concentrations of different species indicated that automobiles were the predominant source of emission of PAHs in this city. From the diagnostic ratios, the results indicated that PAHs in the ambient air in Tehran originate primarily from diesel and gasoline engines, but contribution of the related diesel emission is apparently higher, or dominant, at the sampling sites and therefore considered as the major emission of PAHs in the ambient air.     

Influence of simulated tidal cycles on aromatic hydrocarbon uptake and elimination by the shore crab Hemigrapsus nudus

Specimens of the intertidal crab Hemigrapsus nudus (Dana, 1851) were collected near Sitka, Alaska, in 1983, and were used in laboratory experiments to determine the influence of periodic exposure to air on the toxicity, uptake, and elimination of two aromatic hydrocarbons, toluene and naphthalene. Three tidal cycles were simulated; i.e., crabs spent 0, 33, or 66% of the time in air and the remainder of the time in toxicant or seawater solutions. Naphthalene was 10 to 20 times more toxic than toluene: naphthalene caused locomotory impairment or killed the crabs more rapidly and at concentrations lower than those of toluene. In tissues, naphthalene was accumulated faster and to higher concentrations than was toluene. Exposure to air 66% of the time reduced mortality as well as the rate at which toluene and naphthalene were accumulated and lost. Crabs exposed to air had significantly higher respiration rates than those submerged in seawater; thus, reduced rates of accumulation and loss of hydrocarbons were not caused by general metabolic quiescence. Exposure to air reduced the time the crabs were in contact with water-borne hydrocarbons and, therefore, reduced toxicity of the hydrocarbons. Once crabs had accumulated the toxicants, however, elimination was hindered by exposure to air, even for the somewhat volatile compound toluene.     

青藏高原纳木错流域持久性有机污染物的多介质迁移与归趋模拟

南亚排放的持久性有机污染物(POPs)可随大气传输到青藏高原,然而POPs在高原多介质间的迁移与分配尚不清晰。本研究利用三级逸度模型对4种POPs(六六六α-HCH,滴滴涕p,p'-DDT,菲Phe和苯并芘Ba P)在纳木错流域的迁移与归趋进行了模拟。结果表明,大气沉降是该区域污染物的主要输入过程,而降解损失则是主要的输出途径。就最终归趋而言,土壤是POPs在纳木错流域的重要储库,其存储了大于50%的POPs。此外,湖水和沉积物分别对α-HCH和PAHs具有较强的存储能力。灵敏度分析的结果表明,环境温度、大气中POPs的浓度及其理化性质是影响POPs在环境中分布的关键参数。本研究明确了纳木错流域不同POPs的迁移方向和归趋特征,这将为青藏高原生态安全评估提供科学依据。     

A new multimodal and asymmetric bivariate circular distribution

Multimodal and asymmetric bivariate circular data arise in several different disciplines and fitting appropriate distribution plays an important role in the analysis of such data. In this paper, we propose a new bivariate circular distribution which can be used to model both asymmetric and multimodal bivariate circular data simultaneously. In fact the proposed density covers unimodality as well as multimodality, symmetry as well as asymmetry of circular bivariate data. A number of properties of the proposed density are presented. A Bayesian approach with MCMC scheme is employed for statistical inference. Three real datasets and a simulation study are provided to illustrate the performance of the proposed model in comparison with alternative models such as finite mixture Cosine model.     

A Bayesian hierarchical model for monitoring harbor seal changes in Prince William Sound, Alaska

Bayesian hierarchical models were used to assess trends of harbor seals, Phoca vitulina richardsi, in Prince William Sound, Alaska, following the 1989 Exxon Valdez oil spill. Data consisted of 4–10 replicate observations per year at 25 sites over 10 years. We had multiple objectives, including estimating the effects of covariates on seal counts, and estimating trend and abundance, both per site and overall. We considered a Bayesian hierarchical model to meet our objectives. The model consists of a Poisson regression model for each site. For each observation the logarithm of the mean of the Poisson distribution was a linear model with the following factors: (1) intercept for each site and year, (2) time of year, (3) time of day, (4) time relative to low tide, and (5) tide height. The intercept for each site was then given a linear trend model for year. As part of the hierarchical model, parameters for each site were given a prior distribution to summarize overall effects. Results showed that at most sites, (1) trend is down; counts decreased yearly, (2) counts decrease throughout August, (3) counts decrease throughout the day, (4) counts are at a maximum very near to low tide, and (5) counts decrease as the height of the low tide increases; however, there was considerable variation among sites. To get overall trend we used a weighted average of the trend at each site, where the weights depended on the overall abundance of a site. Results indicate a 3.3% decrease per year over the time period.     

Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.     

Prediction of particulate air pollution from a landfill site using CFD and LIDAR techniques

Landfill sites have been the most common way of eliminating solid urban waste, as well as that of public and mining wastes. Landfill sites are a constant source of environmental pollution and wind is the most important contributing factor to air pollution, due to the erosion which it produces over the landfill site surfaces, transporting dust away from the source point. This causes serious risks for human health and general dirt in the surrounding areas of the landfill site. The result of dust air pollution produced in a landfill site is analysed through CFD3D (Computational Fluid Dynamics) by joining the LIDAR (Light Detection and Ranging) technique and Ansys CFX 10.0 software. The CFD simulations determine the wind velocity distribution on the landfill site surface and the different particle threshold friction velocities which determine the dust emission in multiphase simulations (air-particles). These simulations are validated from field data obtained in three measurement programmes for each type of landfill site surface treatment which has been studied. It was determined that the superficial landfill site treatment with the lowest air pollution is tall grass and bushes. The methodology used can be applied to the dust emission calculation in the design or evaluation of other landfill sites.     

我国几种典型树种非甲烷烃类的排放特征

采用封闭式采样法和带有预浓缩及程序升温装置的熔融毛细管柱气相色谱仪，对我国的松、柏、杨、槐等几种典型树种排放非甲烷烃类（ＮＭＨＣ）的组成和排放速率进行了定性和定量测定，初步确定了不同树种和的特征物种和排放规律，结果表明，针叶树种和阔叶树种排放的ＮＭＨＣ组成差别很大，各树种的排放规律除了与自身的结构和生理活动等在内在因素有关外，还在不同程度上受到外界因素（主要是温度和光强）的影响，一般温度越高，光强     

Sensitivity of Ozone Production to the Nmhc Composition, Aerosol Back-Scattering and High Chimney Emission

In this paper, three sensitivity studies are designed to analyze the effect of the NMHC (Non-Methane HydroCarbon) composition, the aerosol back-scattering and the high chimney NO_x emission to the photochemical prodution of ozone by using a one-dimensional photochemistry-diffusion model under a favourable meteorological condition. Measurements of the NMHC composition in Taipei indicated that the percentage of iso-butene, cis-2-butene, trans-2-butene and benzene in a unit volume was much higher than of those observed in other major cities. the high ratio of benzene was directly linked to its high percentage in gasoline. As to the unusually high amount of iso-butene, cis-2-butene and trans-2-butene, more researches are needed to identify their source. Concerns are raised as to how productive is NMHC composition is to the photochemical production of the surface ozone. A rough estimate shows that the total reactivity of the Taipei NMHC composition is about 1.21 × 10^-9 cm³ s^-1 which is 1.6 times that of the Los Angeles (LA) NMHC composition, while the simulated noon peak will be different by 28 ppbv, i.e. 18% more than that simulated with a LA composition.

Meanwhile, high aerosol loading is a serious problem in Taipei. the attenuation of the UV radiation by aerosols cannot be ignored. A numerical simulation shows that the noon ozone level will decrease from 178 to 141ppbv, i. e. about 21% reduction, with deterioration of the visual range from 85 to 5 km.

In the southern Taiwan, industry parks are mixed with the populated Kaohsiung city, hence the large emission of NO_x from high chimneys cannot be ignored. in this study, NO_x is assumed to be emitted in the layer between 235-460 m high with an emission rate of 0.05 or 0.145 ppbv/sec. the results show that the NO_x emitted from the elevated stack lead to a considerable reduction of surface ozone. Such conclusion is obtained due to the fact that a one-dimensional model is used in this paper. Whereas, if a three-dimensional regional model was used, then a higher productivity of ozone downstream would be simulated.